Method and arrangement for supporting analysis of social networks in a communication network

ABSTRACT

A method and apparatus for supporting social network analysis of terminal users in a communication network. Users being located relatively close to each other when making calls are more likely to be socially “connected” than users having mutually more remote locations. A partitioning unit ( 100 ) determines a representative geographical location for individual users based on traffic data and location data ( 102,104 ). User partitions are then made based on the users&#39; representative geographical locations such that one particular user partition contains users having representative geographical locations within a limited geographical area. The user partition is then provided to a social network analysis function ( 106 ) for further analysis ( 108 ).

TECHNICAL FIELD

The invention relates generally to a method and arrangement forsupporting analysis of social relationships between terminal users in acommunication network.

BACKGROUND

In the field of telecommunication, solutions have been devised forproviding relevant and potentially attractive services that have beenadapted to different service consumers according to their interests andneeds in different situations. These services can thus be customised forindividual users depending on their user profiles and/or currentsituation. Some examples are advertising and personalised TV. Solutionshave also been suggested for identifying groups or “clusters” of usersbelonging to different social networks, and for adapting variousservices to these user groups.

Differentiated adaptation of services for user groups in a communicationnetwork may be accomplished based on knowledge of social relationshipsbetween terminal users. This kind of information may be extracted fromtraffic data available in communication networks, i.e. information onexecuted calls and other sessions, using various methods and algorithmsfor social network analysis which have been developed recently. Further,it may be of interest for some parties to gain knowledge of variousstatistics and aspects related to social networks. Some examples of suchknown analysis techniques are “centrality methods”, i.e. finding themost central user in a social network, and “clique analysis”, i.e.finding a group of users which are closely related to each other in somerespect. Any such methods and algorithms for analysing social networksare often generally referred to as “SNA (Social Network Analysis)algorithms”, which term will be used here as well.

Great amounts of traffic data are generally available from Charging DataRecords (CDR) which are commonly generated and stored for the networksto support charging for executed calls and sessions. The traffic datamay refer to voice calls, SMS (Short Message Service), MMS (MultimediaMessage Service), game sessions and e-mails. In this description, theterm “calls” is used for short to represent any type of communicationbetween two parties, thus without limitation to voice calls. The trafficdata may also contain further information on the calls related to thetime of day, call duration, location and type of service used. Trafficdata can also be obtained by means of various traffic analysing devices,such as Deep Packet Inspection (DPI) analysers and other trafficdetecting devices, which can be installed at various communication nodesin the network.

This traffic data can thus be used to derive information on the socialrelations between different users, depending on the amount and type ofcommunications these users have conducted with each other as well astime of day, duration and location when making their calls and sessions.Great efforts have been made to provide a mechanism for automaticallyidentifying different groups or sets of users which are regarded associally related, based on available traffic data. It has been generallyrecognised that a history of executed calls and sessions betweendifferent users can provide a basis for such social network analysis,basically assuming that two users having executed one or more calls canbe regarded as socially related. Different criteria for calls may bedefined for determining whether two users are deemed socially related,e.g. only calls exceeding 30 seconds to eliminate wrongly diallednumbers, only calls in both directions between two persons to eliminatesupport centres or the like, a minimum call frequency, and so forth.

However, many communication networks of today are quite extensiveserving huge amounts of subscribers, maybe millions, that are typicallyactive in such a large communication network. There is also a rapidincrease of users and traffic in these networks. Therefore, it is a verycomplex and time-consuming process to detect and analyse millions ofcalls and sessions in order to identify different groups or sets ofsocially related users based on their previously executedcommunications.

For example, it may take several hours, days or even weeks for aprocessor system to execute SNA algorithms on a vast source ofinformation on executed calls and sessions, such as traffic data, toidentify a group of socially related users. This work also requiresextensive memory resources for storing huge amounts of data. It is thusa problem that social network analysis of users in a communicationnetwork can be very complex and requires substantial storing, processorand computing resources, and that it also takes much time.

SUMMARY

It is an object of the invention to address at least some of the issuesoutlined above. It is thus an object to facilitate the process ofanalysing social relationships between terminal users in a communicationnetwork. These objects and others can be achieved primarily by asolution according to the appended independent claims.

According to different aspects, a method, an arrangement and a computerreadable medium are defined for supporting analysis of socialrelationships between terminal users in a communication network by meansof a partitioning unit.

In the inventive method, traffic data and location data related to callsmade by the users are obtained, e.g.

from CDR data available in the network. A representative geographicallocation is then determined for individual users based on the trafficdata and location data. Next, a user partition is formed that defines agroup of users based on the determined locations, where the users haverepresentative geographical locations within a predetermined area. Theformed user partition is then finally provided or delivered to a socialnetwork analysis function for further analysis work. In another aspect,a computer readable medium is defined containing instructions which,when executed on a processor in a partitioning unit, performs the methodabove.

According to the inventive arrangement, the partitioning unit comprisesa data obtaining unit adapted to obtain traffic data and location datarelated to calls made by the users, and a location determining unitadapted to determine a representative geographical location forindividual users based on the traffic data and location data. Thepartitioning unit further comprises a partition forming unit adapted toform a user partition that defines a group of users based on thedetermined locations where the users have representative geographicallocations within a predetermined area, and a delivering unit adapted toprovide the user partition to a social network analysis function.

The invented method, arrangement and computer readable medium may beimplemented according to any of the following optional embodiments.

In one embodiment, the user partition is represented as a division on agraph comprising the users. Further, the predetermined area of the userpartition may coincide with a densely populated geographical district orregion.

In another embodiment, a plurality of location based user partitions areformed defining different groups of users having representativegeographical locations within different limited areas. In that case, theuser partitions may be formed according to a predefined networkpartition plan of corresponding predetermined areas, which is used tomap each user's representative location to a user partition. The networkpartition plan may further be adjusted based on feedback from thesubsequent analysis work using the formed user partitions.Alternatively, the user partitions can be formed for clusters of usersfound to be in relatively close proximity to each other according totheir representative locations, and where a relatively small amount ofcalls have been made across different user partitions. The locationbased user partitions may also be adjusted such that the amount ofexecuted calls implying social connections or ties across different userpartitions, is reduced.

In further embodiments, the traffic data and location data can beobtained from CDR:s. Location data may be further extracted frominformation on the geographical location of cells, service and locationareas in the case of a mobile network, or of connection points in whichportable terminals can be plugged in the case of a fixed network. Therepresentative geographical location basically implies a location wherea user mostly makes his/her calls.

Using the inventive method and arrangement above will enable networkoperators to provide useful and accurate information on socialrelationships between terminal users more efficiently in terms of totalprocessing time and scalability as well as storing capacity, whenperforming social network analysis of large communication networks.

Further possible features and benefits of the invention will becomeapparent from the detailed description below.

BRIEF DESCRIPTION OF THE DRAWINGS

The invention will now be described in more detail by means of exemplaryembodiments and with reference to the accompanying drawings, in which:

FIG. 1 is a schematic block diagram illustrating an example of how asocial network analysis can be supported, according to one embodiment.

FIG. 2 is a schematic diagram illustrating an example of how partitionsmay be formed with terminal users in a communication network, when usingthe invention.

FIG. 3 is a flow chart illustrating a procedure for supporting socialnetwork analysis, according to another embodiment.

FIG. 4 is a schematic block diagram illustrating how a partitioning unitmay operate in a practical example, according to further embodiments.

FIG. 5 is a schematic block diagram illustrating an arrangement in apartitioning unit in more detail, according to further embodiments.

DETAILED DESCRIPTION

In this description, the term “user partition” is used to represent thedivision of a plurality of terminal users which are deemed to besocially related in a predefined respect. Briefly described, theinvention may be used to facilitate the analysis work of defining userpartitions and identifying terminal users which are qualified to belongto a certain partition, in this description referred to as the processof partitioning. When a user partition has been formed, it is possibleto execute SNA algorithms for the users within that partition. Further,various communication services and other services may be adapted to theusers in the partition based on the knowledge that these users aredeemed socially related, at least to some extent. It may also bepossible to utilise such knowledge on social networks for planning andconfiguration of the communication network or other communityinfrastructures.

It has been previously recognised that in a common traffic pattern, amajority of calls are made between terminal users when locatedrelatively close to one another, i.e. within a limited geographicalarea, while calls between remotely located users are generally lessfrequent. In this invention, it is further noted that there is a strongcorrelation between the mutual distance or proximity of representativegeographical locations for individual users and the strength of theirsocial relations. As a result, users typically being located relativelyclose to each other when calling are more likely to be socially“connected” than users having mutually more remote locations. In fact,an approximation can be done where the probability that two individualsare socially connected is inversely proportional to the square of theirrelative distance.

This conclusion is now utilised for making user partitions based on theusers' geographical locations when calling each other such that oneparticular partition contains users with representative geographicallocations within a predetermined limited area. In this description, theterm “representative geographical location” implies a location where auser mostly makes his/her calls. A graph of location-based userpartitions may also be created, and these user partitions may be furtheradjusted in order to minimise the amount of social connections, or“ties”, across different user partitions.

The users in these location-based user partitions can thus be analysedin terms of social networks, using SNA algorithms or the like, which ishowever outside the scope of this solution. This further analysis workis thus facilitated when the inventive location-based user partitionshave been made, which is a relatively simple way of grouping userstogether with a high probability of being socially connected. Further,when a great number of users have been divided into a plurality of userpartitions in this way, the SNA algorithms can be executed on eachpartition in parallel with significantly reduced amount of users. As aresult, the execution time, complexity and storage requirements can allbe reduced and the efficiency can thus be generally improved for anysubsequent analysis work within each user partition.

It is also possible to use the above-described location-basedpartitioning as a “pre-partition” for further partitioning work, e.g.using various partitioning algorithms. In fact, some partitioningalgorithms are only effective if some pre-partitioning has been made,and using the inventive location-based partitioning will make thepartitioning algorithms the more effective and the processing time canbe reduced by several hours for large communication networks.

The representative geographical location of a user may be determinedfrom available traffic data, such as CDR data, although other methodsare also possible to use such as positioning functions for mobile usersthat may be employed anyway in the network. Further knowledge on thelocation of different cells, service and location areas of a mobilenetwork, or connection points of a fixed network in which portableterminals can be plugged, etc., may be needed to establish the locationof a user when making a call, depending on what type of network is used.For example, such portable terminals may be used in fixed networks forservices such as e-mail, chatting and Skype. A mobile terminal mayfurther send GPS coordinates which can be used to determine his/herposition.

FIG. 1 illustrates how location-based user partitions with terminalusers potentially being socially related, can be formed for acommunication network by means of a partitioning unit 100, according toan exemplary embodiment. Initially, it is assumed that no partitionshave been made and that traffic data is available for calls orcommunications made in the network, which may be a huge amount of datain an extensive network.

In a first schematic action 1:1, the partitioning unit 100 obtainstraffic data of executed calls and location data of the network from atraffic data source 102 and a location data source 104, respectively.The traffic data source 102 may include a CDR database and/or a trafficsensing function which can register executed calls in the network andprovide useful information on those calls, e.g. by means of varioustraffic analysing devices such as Deep Packet Inspection (DPI) analysersor similar installed at various communication nodes in the network.

The location data source 104 may be a database or the like holdinginformation on the geographical location of different nodes of thenetwork used when making the calls, e.g. connection points in whichportable terminals can be plugged in the case of a fixed network, orcells, service and location areas in the case of a mobile network. Otherlocation data sources may also be used, such as the operator'ssubscription data base holding billing address information ofsubscribers.

From the obtained traffic and location data, the partitioning unit 100then determines a representative geographical location for individualusers in the network, as indicated in a further action 1:2. As mentionedabove, the representative geographical location of a user is thelocation where the user mostly makes his/her calls, which can be derivedfrom the traffic and location data of individual calls as describedabove. For example, the user's location may first be determined with asuitable accuracy for each call, and the most frequent location may thenbe selected as the most representative location of that user.

This procedure is conducted for any number of users in the network, e.g.for practically all users having made a sufficient number of calls toprovide a meaningful representative geographical location. A certainfiltration, or “cleaning”, of data may also be made before this step,e.g. disregarding calls to non-personal communication nodes such asautomated phone services, calls to support numbers and telemarketingactivities.

Within the scope of step 1:2, and depending on the type of network andits configuration, the partitioning unit 100 may derive the users'geographical locations for each call from the traffic data, e.g.received as CDR data, such that further location data from a separatelocation data source 104 may not be necessary. Thus, although thetraffic data source 102 the location data source 104 are illustrated astwo separate entities, they may in practice be realised as a single CDRdatabase.

Depending on the operator-specific configuration of the CDR:s, differentamounts of geographical information can be extracted therefrom. Forexample, a CDR of a mobile network typically includes a “Cell GlobalIdentity” or

“Service Area Identity” referred to as CGI/SAI, containing a globallyunique identity of the cell used for the call. From the CGI/SAI, it isalso possible to derive location information on a higher level, e.g.,location area, BSC (Base Station Controller) service area or MSC (MobileSwitching Centre) service area.

As indicated in a further action 1:3 in the partitioning unit 100, userpartitions that define groups of terminal users with similarrepresentative geographical locations, are then formed based on theusers' locations determined according to the above. In this example, aplurality of such location-based user partitions are thus formeddefining different groups of users having representative geographicallocations within different limited areas. In this step, a predefinednetwork partition plan may be used which outlines different geographicareas for the user partitions. When representative geographicallocations have been determined for the terminal users, the networkpartition plan can thus be used to map their representative locations toa user partition, thereby identifying which users belong to eachpartition. In practice, this partition plan may have been created by anetwork planning expert or generated automatically.

When creating the network partition plan, the number of user partitionstherein is thus decided and what geographical regions they should cover.For example, if the users' representative locations are derived fromtheir CDR:s, the partition plan may state which BSC service area, MSCservice area or even individual cells to be contained in each userpartition. Optionally, the network partition plan may also be adjustedbased on feedback from the subsequent analysis work using the formeduser partitions.

It is also possible to employ other partitioning strategies withoutrelying on a predefined network partition plan. For example, a userpartition may be formed when a cluster of users are found to be inrelatively close proximity to each other according to their determinedrepresentative locations, and where a relatively small amount of callshave been made across different user partitions. On the other hand,there may be many calls occurring across two particular dense areas inwhich case a partition may be formed covering both these areas.

In some practical cases, it may not be necessary to determine the user'sexact location at the time of each call. It may be sufficient todetermine a typical location of the user over a certain time, e.g. onemonth, as the representative geographical location. For example, if alarge number of CDR:s is available for a user in a mobile network, themost frequently occurring CGI/SAI of the CDR:s during the month may beselected as the representative location. In this context, a cellidentity may be too detailed location information. It may be better toform the user partition based on an RNC/BSC (Base Station Controller)service area, or even the MSC service area, depending on the networkplanning of the total area to be covered. For example, if each BSCservice area corresponds to a densely populated geographical district orregion such as a city, it may be suitable to form user partitions basedon which BSC service area the users' representative location belongs to.

In a final shown action 1:4 in the partitioning unit 100, the userpartitions formed in action 1:3 are provided or delivered to a socialnetwork analysis function 106 or the like for executing various SNAalgorithms 108 in order to obtain useful information and knowledge ofthe social networks of the terminal users in the analysed communicationnetwork. The operation of such a social network analysis function ishowever outside the scope of this solution.

An example of how location-based user partitions may be formed withterminal users in a communication network, will now be described withreference to FIG. 2. It is assumed that representative geographicallocations have been determined for the terminal users as described foraction 1:2 above. FIG. 2 illustrates how the users' representativelocations are distributed over a geographical area covered by thecommunication network. The various arrows between the users illustrateschematically that different calls have been made, which is informationthat can be obtained from traffic data such as CDR:s as described above.For example, user X has called user Y at least once and has been calledat least once by user Z, as shown in the figure.

The user partitions may be represented as divisions on a graph with theusers' representative locations, basically as shown in FIG. 2. It shouldbe noted that this figure is greatly simplified and in reality thenumber of users and calls is of a much greater magnitude. The callsshown by the arrows thus imply probable social relations between theusers.

In this example, three user partitions A, B and C with different usershave been formed such that the number of potential relations acrossdifferent user partitions is as few as possible. These user partitionsA-C may be made from a predefined network partition plan with differentgeographic areas for the partitions, e.g. the areas of cities or otherdensely populated geographical districts or regions. The users are thenmapped and assigned to the user partitions according to theirrepresentative locations and the partition plan.

Alternatively, the user partitions A-C may be formed for clusters ofusers found to be in relatively close proximity to each other accordingto their representative locations, as mentioned above. Using thisapproach, the user partitions A-C may be formed such that a relativelysmall amount of calls have been made across different user partitions,as apparent in the figure. This operation can also be referred to as“minimising the edge cut” of the partitioning.

Using the knowledge of correlation between the proximity ofrepresentative geographical locations for individual users and thestrength of their social relations, this location-based division of theusers in user partitions A-C thus indicates groups of users likely to besocially connected.

A procedure for supporting social network analysis of terminal users ina communication network, will now be described with reference to theflow chart FIG. 3. The shown steps are executed by a partitioning unitsuch as the unit 100 described above. Although this procedure describesthat one user partition is formed, it can be repeated for further userpartitions which may be represented as divisions on a graph or the likewith plural location-based user partitions, e.g. basically in the mannershown in FIG. 2.

In a first step 300, the partitioning unit obtains traffic data andlocation data related to communications made by the users, basicallycorresponding to action 1:1 above. In a next step 302, a representativegeographical location is determined for a plurality of individual usersbased on the obtained traffic data and location data, basicallycorresponding to action 1:2 above. A user partition that defines a groupof users is then formed based on the determined locations, in a furtherstep 304, where the users have representative geographical locationswithin a predetermined area. Finally, in a last step 306, the formeduser partition is provided or delivered to a social network analysisfunction, basically corresponding to action 1:2 above. The socialnetwork analysis function is then able to execute various SNA algorithmson the users in the formed user partition involving a limited number ofusers which will reduce the complexity of the analysis work. Asmentioned above, this process can be repeated to form a plurality ofuser partitions. Thereby, these SNA algorithms are executed on asignificantly reduced amount of users within each partition, as comparedto the total amount of users in the communication network.

A practical example of how a partitioning unit may operate whensupporting social network analysis of terminal users in a communicationnetwork according to this solution, will now be described with referenceto FIG. 4. The partitioning unit 400 operates basically in the mannerdescribed for FIGS. 1 and 3 above, and the example of FIG. 4 outlinesfurther possible features and details. The communication network is acellular mobile network employing CDR:s and maintaining information onwhere individual cells are positioned geographically. As mentionedabove, cell positions may be used as location information for theindividual users' representative geographical locations in this context,although it may in some situations be sufficient and more suitable touse an RNC/BSC service area, or even an MSC service area, as locationinformation for terminal users in a cellular network depending on thenetwork planning of cells.

A first action 4:1 in the partitioning unit 400 illustrates thatposition information is collected from various data sources, in thiscase including traffic data from a CDR database 402, and location datafrom a cell position database 404 and/or a subscriber address database406, although all of these data sources are not necessarily utilised.For example, it may be sufficient to identify a user of a call and whichcell the user was located in when making the call, from a CDR for thatcall, and to determine that cell's geographical position from database406. In another feasible example, a calling user may be identified fromthe CDR and that user's home address may be obtained from database 406to be used as the user's representative geographical location.

In a next action 4:2, the user's representative geographical locationsare determined from the information obtained in the preceding action4:1. In a further action 4:3, a graph G is initialized on which userpartitions A, B, C, . . . are to be presented as divisions, and thisaction may be executed at any time before the next action 4:4. Whenpopulated with terminal users, the graph G may be configured basicallyas shown in FIG. 2.

Thus, the partitioning unit 400 forms the user partitions A, B, C, . . .in the next action 4:4, based on the determined representative userlocations and using the initialised graph G, in this example also usinga predefined network partition plan 400 a with different geographicareas for the user partitions. It is thus assumed that the partitionplan 400 a has been predefined in the partitioning unit 400, and it mayoutline the areas of cities or other densely populated geographicaldistricts or regions as the user partitions A, B, C, . . . . The usersare thus mapped and assigned to the user partitions A, B, C, . . . ofthe network partition plan according to their determined representativelocations.

In this action, the previously initialized graph G is also populatedwith the users according to their partition assignment above, thuscreating a populated graph G′. In a final action 4:5, the formed userpartitions A, B, C, . . . are output as the populated graph G′(A, B, C,. . . ) to a social network analysis function or the like, not shown inthis figure. Optionally, the network partition plan can be adjustedbased on feedback from the subsequent analysis work when having used theformed user partitions A, B, C, . . . , as indicated by the dashedarrow.

A possible arrangement in a partitioning unit will now be described inmore detail with reference to the block diagram in FIG. 5, which may beused in any of the above-described procedures and embodiments. Thepartitioning unit 500 is thus configured to support social networkanalysis of terminal users in a communication network, and comprises adata obtaining unit 500 a adapted to obtain traffic data D and locationdata L related to calls made by the users. The traffic data D andlocation data L are obtained from suitable data sources not shown inthis figure. The partitioning unit 500 also comprises a locationdetermining unit 500 b adapted to determine a representativegeographical location for individual users based on the obtained trafficdata and location data.

The partitioning unit 500 further comprises a partition forming unit 500c adapted to form a user partition that defines a group of users basedon the determined locations where the users have representativegeographical locations within a predetermined area. The partitioningunit 500 also comprises a delivering unit 500 d adapted to provide theuser partition to a social network analysis function.

Optionally, the different functional units in the partitioning unit 500can be further configured according to the following examples. Thepartition forming unit 500 c may be further adapted to represent theuser partition as a division on a graph comprising the users. Thepartition forming unit 500 c may be further adapted to form a pluralityof location based user partitions defining different groups of usershaving representative geographical locations within different limitedareas.

The partition forming unit 500 c may be further adapted to form the userpartitions according to a predefined network partition plan ofcorresponding predetermined areas, by using the partition plan to mapeach user's representative location to a user partition. The partitionforming unit 500 c may be further adapted to adjust the networkpartition plan based on feedback from the subsequent analysis work usingthe formed user partitions. The partition forming unit 500 c may befurther adapted to adjust the location based user partitions byminimising the amount of executed calls implying social connections orties across different user partitions. If no predefined networkpartition plan is used, the partition forming unit 500 c may be adaptedto form the user partitions for clusters of users found to be inrelatively close proximity to each other according to theirrepresentative locations, and where a relatively small amount of callshave been made across different user partitions.

The data obtaining unit 500 a may be further adapted to obtain thetraffic data and location data from CDR:s. The data obtaining unit 500 amay be further adapted to extract location data from information on thegeographical location of connection points in which portable terminalscan be plugged in the case of a fixed network, or of cells, service andlocation areas in the case of a mobile network.

It should be noted that FIG. 5 merely illustrates various functionalunits in the partitioning unit 500 in a logical sense, although theskilled person is free to implement these functions in practice usingany suitable software and hardware means. Thus, the invention isgenerally not limited to the shown structure of the partitioning unit500 while its functional units may be configured to operate according tothe methods and procedures described above for FIGS. 1-4, whereappropriate.

The invention may be implemented as a computer readable mediumcontaining instructions which when executed on a processor in apartitioning unit performs the following steps, to support socialnetwork analysis of terminal users in a communication network: A firststep of obtaining traffic data and location data related to calls madeby the users, a next step of determining a representative geographicallocation for individual users based on the traffic data and locationdata, a next step of forming a user partition that defines a group ofusers based on the determined locations, where the users haverepresentative geographical locations within a predetermined area, and afinal step of providing the user partition to a social network analysisfunction.

By using the invention according to any of the above-described aspectsand embodiments, the social analysis of users in a communication networkcan be supported and facilitated with reduced complexity and requiringless processor and computing resources. In particular, geographicalinformation already available in traffic data such as the CDR:s, isutilised in the above-described manner, e.g. to provide a graph withuser partitions comprising users which are likely to be sociallyconnected. As a result, the social analysis may be carried out for eachuser partition separately in much shorter time, as compared to thepreviously known solutions. The above-described location-basedpartitioning procedure may be used to create a “pre-partitioning” to beused for further partitioning work, e.g. using various partitioningalgorithms which is however outside the scope of this invention.

While the invention has been described with reference to specificexemplary embodiments, the description is generally only intended toillustrate the inventive concept and should not be taken as limiting thescope of the invention. The invention is defined by the appended claims.

The invention claimed is:
 1. A method executed by a partitioning devicefor supporting social network analysis of terminal users in acommunication network, comprising: obtaining, from data recordsavailable in the communication network, traffic data and location datarelated to calls made by said users during a period of time, wherein thetraffic data and location data related to any calls made by a given userduring the period of time indicate one or more locations at which thatgiven user made those calls; determining a representative geographicallocation for individual ones of said users based on said traffic dataand location data, wherein the representative geographical location of auser is a location representative of where the user more often than notmakes his or her calls, as indicated by the traffic data and locationdata obtained for calls made by that given user during the period oftime; forming a user partition that defines a group of said users havingrepresentative geographical locations within a predetermined area; andproviding said user partition to a social network analysis function. 2.The method according to claim 1, wherein said user partition isrepresented as a division on a graph comprising said users.
 3. Themethod according to claim 1, wherein said predetermined area coincideswith a densely populated geographical district or region.
 4. The methodaccording to claim 1, wherein said forming further comprises forming aplurality of location-based user partitions that define different groupsof said users having representative geographical locations withindifferent limited areas.
 5. The method according to claim 4, whereinsaid forming comprises forming the location-based user partitionsaccording to a predefined network partition plan of correspondingpredetermined areas, by using the network partition plan to map eachuser's representative geographical location to a location-based userpartition.
 6. The method according to claim 5, further comprisingadjusting the network partition plan based on feedback from the socialnetwork analysis function regarding social network analysis done usingthe formed location-based user partitions.
 7. The method according toclaim 4, wherein said forming comprises forming said location-based userpartitions to include clusters of users in relatively close proximity toeach other according to their representative geographical locations, andto form said user partitions such that a relatively small number ofcalls have been made across different user partitions.
 8. The methodaccording to claim 4, further comprising adjusting said location-baseduser partitions to reduce the amount of calls executed across differentuser partitions, and thereby reduce the number of implied socialconnections or ties across different user partitions.
 9. The methodaccording to claim 1, wherein said obtaining comprises obtaining thetraffic data and location data from charging data records.
 10. Themethod according to claim 9, wherein said obtaining further comprisesextracting location data from at least one of: information on thegeographical location of cells; service and location areas in the casethe communication network is a mobile network; and connection points atwhich portable terminals connect to the communication network in thecase the communication network is a fixed network.
 11. A partitioningdevice configured to support social network analysis of terminal usersin a communication network, comprising a processor and a memorycontaining instructions executable by said processor whereby said deviceis operative to: obtain, from data records available in thecommunication network, traffic data and location data related to callsmade by said users during a period of time, wherein the traffic data andlocation data related to any calls made by a given user during theperiod of time indicate one or more locations at which that given usermade those calls; determine a representative geographical location forindividual ones of said users based on said traffic data and locationdata, wherein the representative geographical location of a user is alocation representative of where the user more often than not makes hisor her calls, as indicated by the traffic data and location dataobtained for calls made by that given user during the period of time;form a user partition that defines a group of users havingrepresentative geographical locations within a predetermined area; andprovide said user partition to a social network analysis function. 12.The partitioning device according to claim 11, wherein the partitioningdevice is further configured to represent said user partition as adivision on a graph comprising said users.
 13. The partitioning deviceaccording to claim 11, wherein said predetermined area coincides with adensely populated geographical district or region.
 14. The partitioningdevice according to claim 11, wherein the partitioning device is furtherconfigured to form a plurality of location-based user partitionsdefining different groups of users having representative geographicallocations within different limited areas.
 15. The partitioning deviceaccording to claim 14, wherein the partitioning device is furtherconfigured to form said user partitions according to a predefinednetwork partition plan of corresponding predetermined areas, by usingthe partition plan to map each user's representative location to alocation-based user partition.
 16. The partitioning device according toclaim 15, wherein the partitioning device is further configured toadjust the network partition plan based on feedback from the socialnetwork analysis function regarding social network analysis done usingthe formed location-based user partitions.
 17. The partitioning deviceaccording to claim 15, wherein the partitioning device is furtherconfigured to adjust said location-based user partitions by minimizingthe amount of calls executed across different user partitions, andthereby minimize the number of implied social connections or ties acrossdifferent user partitions.
 18. The partitioning device according toclaim 14, wherein the partitioning device is further configured to formsaid user partitions to include clusters of users in relatively closeproximity to each other according to their representative locations, andto form said user partitions such that a relatively small number ofcalls have been made across different user partitions.
 19. Thepartitioning device according to claim 11, wherein the partitioningdevice is configured to obtain the traffic data and location data fromcharging data records.
 20. The partitioning device according to claim19, wherein the partitioning device is further configured to extractlocation data from at least one of: information on the geographicallocation of cells; service and location areas in the case thecommunication network is a mobile network; and connection points atwhich portable terminals connect to the communication network in thecase the communication network is a fixed network.
 21. method of claim1, wherein the method further comprises: forming a plurality of userpartitions, wherein each partition defines users that are likely to besocially connected; and providing the plurality of user partitions to asocial network analysis function, wherein providing said user partitionsto a social network analysis function assists the social networkanalysis function to analyze, for each partition, the actual existenceand/or extent of social connections amongst users in the same partition.22. The method of claim 1, wherein said determining comprises excluding,based on the obtained traffic data, calls from a set of calls withobtained location data, and determining a representative geographiclocation based on the location data of remaining calls in the set. 23.The method of claim 1, wherein traffic data comprises how frequentlycalls were made by a user with respect to a plurality of locationsidentified in the location data, and determining a representativegeographical location for a user comprises determining based on thelocation that had the most frequent calls.
 24. A method of facilitatinganalysis of social connections amongst a population of terminal users ina communication network, the method comprising: for each of the terminalusers in said population, obtaining, from data records available in thecommunication network, data indicating locations that the user haspreviously been at when participating in communication sessions via thecommunication network during a period of time and determining, based onthat data, a representative geographical location for the user whichrepresents the user as more often than not being at that location whenparticipating in communication sessions, as indicated by the dataobtained for the user; partitioning said population of users intodifferent partitions of users that are likely to be socially connected,by defining different partitions as including different groups of usershaving representative geographical locations within different limitedareas; and assisting a social network analysis function to analyze, foreach of said partitions, the actual existence and/or extent of socialconnections amongst users in that same partition, rather than analyzingthe actual existence and/or extent of social connections amongst allusers in said population as a whole, by sending information indicatingeach of said partitions to the social network analysis function.
 25. Themethod according to claim 1, wherein the data records include chargingdata records, data records generated by one or more traffic analyzingdevices in the communication network, data records generated by one ormore positioning functions in the communication network for determiningthe position of users, or any combination thereof.
 26. The partitioningdevice according to claim 11, wherein the data records include chargingdata records, data records generated by one or more traffic analyzingdevices in the communication network, data records generated by one ormore positioning functions in the communication network for determiningthe position of users, or any combination thereof.
 27. The methodaccording to claim 24, wherein the data records include charging datarecords, data records generated by one or more traffic analyzing devicesin the communication network, data records generated by one or morepositioning functions in the communication network for determining theposition of users, or any combination thereof.